We propose CAPGrasp, an $\mathbb{R}^3\times \text{SO(2)-equivariant}$ 6-DoF continuous approach-constrained generative grasp sampler. It includes a novel learning strategy for training CAPGrasp that eliminates the need to curate massive conditionally labeled datasets and a constrained grasp refinement technique that improves grasp poses while respecting the grasp approach directional constraints. The experimental results demonstrate that CAPGrasp is more than three times as sample efficient as unconstrained grasp samplers while achieving up to 38% grasp success rate improvement. CAPGrasp also achieves 4-10% higher grasp success rates than constrained but noncontinuous grasp samplers. Overall, CAPGrasp is a sample-efficient solution when grasps must originate from specific directions, such as grasping in confined spaces.

翻译：我们提出了CAPGrasp，一种$\mathbb{R}^3\times \text{SO(2)-等变}$的6自由度连续接近约束生成式抓取采样器。该方法包含一种新颖的训练策略，无需构造大规模条件标注数据集，以及一种在保持抓取接近方向约束的同时改善抓取姿态的约束型抓取精炼技术。实验结果表明，CAPGrasp的采样效率比无约束抓取采样器高出三倍以上，同时抓取成功率提升高达38%。与有约束但非连续的抓取采样器相比，CAPGrasp的抓取成功率也提高了4-10%。总体而言，CAPGrasp在抓取必须从特定方向出发的场景（例如在受限空间中抓取）中是一种高效的采样解决方案。